---
title: "Attaques des requins"
author: "Laurence et Shaimma"
output:
flexdashboard::flex_dashboard:
navbar:
- { title: "Source", align:right, href: "https://www.kaggle.com/code/jamesssd7245/most-dangeurous-sharks-countries-worldmap-analysis"}
source_code: embed
theme: journal
orientation: columns
vertical_layout: fill
params:
setup_path: ../resources/
---
<style>
.navbar {
background-color:#46ACC8;
border-color:#46ACC8;
}
.navbar-brand {
color:black!important;
}
</style>
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")
library(RColorBrewer)
library("ggnewscale")
library(ggrepel)
library(wesanderson)
library(cowplot)
```
```{r, include=FALSE}
# mise en forme
requin <- read.csv("attacks.csv")
req <- requin %>%
select("Year", "Type", "Country", "Area","Activity", "Fatal..Y.N.") %>%
filter(Year >= 1930)
```
Présentation {data-icon="fas fa-mug-saucer"}
=====================================
Columns {data-width=400}
-----------------------------------------------------------------------
```{r picture, echo = F, fig.cap = "Title", out.width = '100%'}
knitr::include_graphics("pngegg-2.png")
```
Columns {data-width=700}
-----------------------------------------------------------------------
```{r,include=FALSE}
req$Country <- tolower(req$Country)
req_pie <- req
req_pie[which(req_pie$Fatal..Y.N. !="Y"),]$Fatal..Y.N. <- "Autres"
req_pie[which(req_pie$Type == "Questionable"),]$Type <- "Invalid"
# netoyage du jeu de donnée
# diving
for (i in (1:nrow(req_pie))) {
if (grepl("diving", req_pie$Activity[i]) | (grepl("Diving", req_pie$Activity[i])) |
(grepl("Dived", req_pie$Activity[i]))) {
req_pie$Activity[i] <- "Diving"
}
}
# fishing
for (i in (1:nrow(req_pie))) {
if (grepl("fishing", req_pie$Activity[i]) | (grepl("Fishing", req_pie$Activity[i])) |
(grepl("lobsters", req_pie$Activity[i])) |
grepl("fish", req_pie$Activity[i]) |
grepl("bichiques", req_pie$Activity[i]) |
grepl("Casting", req_pie$Activity[i]) |
grepl("sardines", req_pie$Activity[i]) |
grepl("Crabbing", req_pie$Activity[i]) |
grepl("Clamming", req_pie$Activity[i]) |
grepl("turtle", req_pie$Activity[i]) |
grepl("prawns", req_pie$Activity[i])|
grepl("net", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "fishing"
}
}
# air or sea disaster
for (i in (1:nrow(req_pie))) {
if (grepl("disaster", req_pie$Activity[i]) | grepl("Disaster", req_pie$Activity[i])|
(grepl("air", req_pie$Activity[i])) | (grepl("plane", req_pie$Activity[i]))
| (grepl("757", req_pie$Activity[i]))|
grepl("hurricane", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "Air/Sea disaster"
}
}
# boat/ship accident de bateau
for (i in (1:nrow(req_pie))) {
if (grepl("Boat", req_pie$Activity[i]) | (grepl("boat", req_pie$Activity[i]))
| (grepl("ship", req_pie$Activity[i])) | (grepl("Fell", req_pie$Activity[i]))
| (grepl("Vessel", req_pie$Activity[i])) |
grepl("sank", req_pie$Activity[i])|
grepl("promotional film ", req_pie$Activity[i]) |
grepl("Ferry", req_pie$Activity[i])|
grepl("ferry", req_pie$Activity[i])|
grepl("capsiz", req_pie$Activity[i]) |
grepl("MV Dona Marilyn", req_pie$Activity[i]) |
grepl("schooner Elizabeth", req_pie$Activity[i]) |
grepl("Towing", req_pie$Activity[i]) |
grepl("Tzenny Chandris", req_pie$Activity[i]) |
grepl("Wreck", req_pie$Activity[i]) |
grepl("wreck", req_pie$Activity[i]) |
grepl("dinghy", req_pie$Activity[i]) |
grepl("yacht", req_pie$Activity[i]) |
grepl("Chumming", req_pie$Activity[i]) |
grepl("raft", req_pie$Activity[i]) |
grepl("Tagging", req_pie$Activity[i]) |
grepl("overboard", req_pie$Activity[i]) |
grepl("free the shark", req_pie$Activity[i]) |
grepl("rescue a shark", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "boat/ship accident"
}
}
# sport
for (i in (1:nrow(req_pie))) {
if (grepl("surf", req_pie$Activity[i]) | grepl("Surf", req_pie$Activity[i]) |(grepl("boarding", req_pie$Activity[i]))|
grepl("Boarding", req_pie$Activity[i]) |
(grepl("batching", req_pie$Activity[i]))| (grepl("Bathing", req_pie$Activity[i])) |
(grepl("bathing", req_pie$Activity[i])) |
(grepl("Swim", req_pie$Activity[i]))| (grepl("swim", req_pie$Activity[i])) |
(grepl("fell", req_pie$Activity[i])) | (grepl("Paddling", req_pie$Activity[i]))|
(grepl("Paddling", req_pie$Activity[i])) | (grepl("water", req_pie$Activity[i]))
| (grepl("Snorkeling", req_pie$Activity[i]))
| (grepl("Sitting", req_pie$Activity[i])) |
grepl("Happy Jack", req_pie$Activity[i]) |
grepl("Bending", req_pie$Activity[i]) |
grepl("Canoe", req_pie$Activity[i]) |
grepl("paddling", req_pie$Activity[i]) |
grepl("Kaya", req_pie$Activity[i]) |
grepl("Lifesaving", req_pie$Activity[i]) |
grepl("Snorkeling", req_pie$Activity[i]) |
grepl("Stamding", req_pie$Activity[i])|
grepl("Wading", req_pie$Activity[i]) |
grepl("Wash", req_pie$Activity[i]) |
grepl("Workong", req_pie$Activity[i])|
grepl("McIver", req_pie$Activity[i]) |
grepl("Standing", req_pie$Activity[i]) |
grepl("Crossing the bay", req_pie$Activity[i])|
grepl("Walking", req_pie$Activity[i]) |
grepl("kill a shark", req_pie$Activity[i]) |
grepl("Removing shark", req_pie$Activity[i]) |
grepl("Riding", req_pie$Activity[i]) |
grepl("Board sailing", req_pie$Activity[i])|
grepl("Exercising", req_pie$Activity[i]) |
grepl("Feeding sharks", req_pie$Activity[i]) |
grepl("Filming", req_pie$Activity[i]) |
grepl("ski", req_pie$Activity[i]) |
grepl("Jumping", req_pie$Activity[i]) |
grepl("Paddle", req_pie$Activity[i]) |
grepl("Play", req_pie$Activity[i]) |
grepl("Resting", req_pie$Activity[i]) |
grepl("floatation device", req_pie$Activity[i])|
grepl("Sculling", req_pie$Activity[i]) |
grepl("Splashing", req_pie$Activity[i]) |
grepl("Dr. Marais", req_pie$Activity[i]) |
grepl("Wrangling", req_pie$Activity[i]) |
grepl("Photographing", req_pie$Activity[i]) |
grepl("SUP", req_pie$Activity[i]) |
grepl("Kakay", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "sport/ activity" }
}
# adrift
for (i in (1:nrow(req_pie))) {
if (grepl("Adrift", req_pie$Activity[i]) | grepl("adrift", req_pie$Activity[i]) |
grepl("Floating", req_pie$Activity[i])|
grepl("3 men & 2 boys", req_pie$Activity[i]) |
grepl("float", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "Adrift"
}
}
# murder / war
for (i in (1:nrow(req_pie))) {
if (grepl("Murder", req_pie$Activity[i]) ||
grepl("submarine", req_pie$Activity[i]) |
grepl("American", req_pie$Activity[i]) |
grepl("torpedoed", req_pie$Activity[i]) |
grepl("Torpedoed", req_pie$Activity[i]) |
grepl("soldiers", req_pie$Activity[i]) |
grepl("Escaping", req_pie$Activity[i]) |
grepl("Japanese", req_pie$Activity[i]) |
grepl("Japan", req_pie$Activity[i]) |
grepl("Sinking", req_pie$Activity[i]) |
grepl("SS", req_pie$Activity[i]) |
grepl("foundered", req_pie$Activity[i]) |
grepl("US", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "murder / war "
}
}
```
```{r}
req_pres <- req_pie %>%
mutate("Year" = as.factor(Year), "Country" = as.factor(Country),
"Type" = as.factor(Type), "Activity" = as.factor(Activity),
"Consequence"= as.factor(Fatal..Y.N.))
```
<h4 style="color : Dark Blue;">
Il existe une cinquaintaine d'espèces de requins dans la Méditerranée, et une centaine d'espèce dans l'Atlantique. <br>
La plupart de ces espèces sont innofensives et trop petits pour constituer une réelle menace pour l'homme. <br> <br>
Cependant, certaines espèces, dangereuses, peuvent être à l'origine de dégàts importants suite à leur interraction avec l'homme. <br>
Nous tentons, dans cette étude, de visualiser les pays les plus touchés par les requins, et d'identifier les natures de ces attaques.
Jeu de donnée à notre disposition:
- le nombre d’attaque du requin par pays et par année <br>
- la nature de l'attaque (Type) <br>
- l’activité du victime lors de l’attaque du requin <br>
- la conséquence de l’attaque ('mortel' ou 'non mortel')
</h4>
```{r}
summary(req_pres)
```
Vision Globale {data-icon="fas fa-map"}
=====================================
Columns {data-width=600}
-----------------------------------------------------------------------
### Carte mondiale: Nombre totale d'attaques par les requins entre 1930 et 2018
```{r, include=FALSE}
########################################################
# get right dataset for world map construction
########################################################
#===================================
## read files
#===================================
requin <- read.csv("attacks.csv")
req <- requin %>%
select("Year", "Type", "Country", "Area","Activity", "Fatal..Y.N.") %>%
filter(Year >= 1930)
# select columns necessary for the construction of the world map
req_map <- req %>%
group_by(Country) %>%
summarize(nb_attaque = n())
# read base map
world <- ne_countries(scale = "medium", returnclass = "sf")
world <- world %>% select(admin, geometry) # for countries
fond <- st_read(dsn = "world-administrative-boundaries/world-administrative-boundaries.shp",
stringsAsFactors = F) # for regions
#===================================
## modify element
#===================================
# change countries names and site to lower
req_map$Country <- tolower(req_map$Country)
world$admin <- tolower(world$admin)
fond$name <- tolower(fond$name)
# standardise site name
req_map[req_map$Country == "columbia" ,]$Country <- "colombia"
req_map[req_map$Country == "curacao" ,]$Country <- "curaçao"
req_map[req_map$Country == "egypt / israel" ,]$Country <- "egypt" # israel
req_map[req_map$Country == "grand cayman",]$Country <- "cayman islands"
req_map[req_map$Country == "iran / iraq",]$Country <- "iran" # "iraq"
req_map[req_map$Country == "italy / croatia" ,]$Country <- "iran" # "italy"
req_map[req_map$Country == "maldive islands" ,]$Country <- "maldives"
req_map[req_map$Country == "micronesia" ,]$Country <- "federated states of micronesia"
req_map[req_map$Country == "nicaragua " ,]$Country <- "nicaragua"
req_map[req_map$Country == "palestinian territories" ,]$Country <- "palestine"
req_map[req_map$Country == "solomon islands / vanuatu" ,]$Country <- "solomon islands" # et vanatu
req_map[req_map$Country == "tanzania" ,]$Country <- "united republic of tanzania"
req_map[req_map$Country == "sudan?" ,]$Country <- "sudan"
req_map[req_map$Country == "trinidad & tobago" ,]$Country <- "trinidad and tobago"
req_map[req_map$Country == "turks & caicos" ,]$Country <- "turks and caicos island"
req_map[req_map$Country == "united arab emirates (uae)" ,]$Country <- "united arab emirates"
req_map[req_map$Country == "usa" ,]$Country <- "united states of america"
req_map[req_map$Country == "western samoa" ,]$Country <- "samoa"
# get site present in csv "world"
a <- req_map$Country
b <- world$admin
present_in_world <- a[which(a %in% b == T)]
# get site present in csv "fond"
a <- as.vector(factor(req_map$Country))
bbis <- as.vector(factor(fond$name))
present_in_fond <- a[which(a %in% bbis == T)]
# site present in both
present <- union(present_in_world , present_in_fond )
dif <- setdiff(present_in_world ,present_in_fond) # site present in fond and not in world
#===================================
## get the right dataset for countries and regiop
#===================================
# find geometry necessary for some site in the base fond
geometry_fond <- fond %>%
filter(fond$name %in% dif) %>%
select("admin"= name, "geometry")
geom_init <- rbind(world, geometry_fond)
# for every site, calculate totale attacks
attack_by_site <- req_map %>%
group_by(Country) %>%
summarise(nb_att_tot = sum(nb_attaque)) %>%
select("admin"= Country, "attacks"= nb_att_tot)
# get dataset with geometry and total attacks of every site
geom <- merge(geom_init , attack_by_site, by="admin", all.x=T)
########################################################
## make dataset for oceans and seas
########################################################
# standardisation
req_map[req_map$Country == "north atlantic ocean " ,]$Country <-"north atlantic ocean"
req_map[req_map$Country == "pacific ocean " ,]$Country <-"pacific ocean"
req_map[req_map$Country == "red sea / indian ocean" ,]$Country <-"indian ocean"
req_map[req_map$Country == "central pacific" ,]$Country <-"pacific ocean"
req_map[req_map$Country == "southwest pacific ocean" ,]$Country <-"south pacific ocean"
req_map[req_map$Country == "pacific ocean" ,]$Country <-"mid-pacific ocean"
# locate ocean/sea/gulf site present in attacks.csv
oce_mer <- c("caribbean sea" , "gulf of aden", "indian ocean",
"mid atlantic ocean" ,"mid-pacifc ocean",
"north atlantic ocean","north pacific ocean","north sea",
"northern arabian sea", "mid-pacific ocean",
"persian gulf", "red sea", "south atlantic ocean", "south china sea",
"south pacific ocean" )
#=========================================================================================
## get element necessary for every site: latitude, longitude, nb tot of attacks
#=========================================================================================
lat <- c(14.939588401460744, 12.579150350346652, -31.65256869194269,
0.0012874607478224015, 0.5168641856999522,
37.55840704949698,26.740062627514437, 56.89226369685983,
13.52913765091261, 6.647930428748582,
27.39827299362649, 21.182319274300287, -32.004663545742, 17.010418251252442,
-34.74671641096358)
long <- c(-74.7792643588162, 47.96353180808757,81.67236879838734,
-19.9999143509654, -150.4849986612673,
-39.08714830907774, -169.80601017384916, 3.676283675031177,
64.31588270602367, -163.13809314592606,
51.41987343745529, 38.424038461264196, -15.840314716043286, 113.41267801349872,
-139.57163512902778)
attacks_oce_mer <- req_map %>%
filter(Country %in% oce_mer) %>%
group_by(Country) %>%
summarise(nb_att_tot = sum(nb_attaque)) %>%
select("oce_mer"= Country, "attacks"= nb_att_tot)
#========================================
# make dataframe
#========================================
omg_init <- data.frame(oce_mer, lat, long)
omg <- merge(omg_init, attacks_oce_mer, by="oce_mer")
geom_test <- data.frame("admin"= c(geom$admin, omg$oce_mer),
"lat" = c(rep(NA, 241), omg$lat),
"long" = c(rep(NA, 241), omg$long),
"attacks" = c(geom$attacks, omg$attacks))
########################################################
## still have some site without geometry or localisation
########################################################
all.countries <- req_map$Country
all.countries[which( ( all.countries %in% oce_mer| all.countries%in% present )== F)]
########################################################
## world map
########################################################
map <- geom %>%
ggplot() +
geom_sf(aes(fill = attacks)) +
scale_fill_distiller(palette = "YlGnBu", trans= "log", direction = 1,
name="Nombre d'attaque",
labels = function(x) sprintf("%.f", x),
breaks = c(1, 7, 55, 403, 1954),
na.value="#edeef2")
# by default, scale_fill_distiller direction is 1
# ==================
# Formatted map
# =================
map2 <- map +
theme_void() +
ggtitle("Nombre totale d'attaques par les requins entre 1930 et 2018") +
#guides(fill = guide_legend(reverse = T))+
theme(text = element_text(family = 'Gill Sans') # mettre le texte en blanc et type de texte
,plot.title = element_text(size = 11)
,panel.background = element_rect(fill = '#b3e2f5') # ajouter couleur du fond de carte
#,plot.background = element_rect(fill = '#333333') # ajouter couleur de fond
,legend.position="bottom")
# ============================
# add oceans /sea/ gulf point
# ===========================
map3 <- map2 +
new_scale_color() +
geom_point(data = geom_test,
aes(x=long, y=lat, col = attacks, fill=attacks),
colour = "black", pch=21, size=2, show.legend = T) +
# geom_text_repel(data = geom_test, aes(x=long, y=lat, label=admin),
# point.padding = 0.5,
# segment.color = 'white',
# colour = "#394b85",
# size=3)+
scale_color_distiller(palette = "YlOrRd", trans= "log", direction = 1 )
```
```{r}
map3
```
Columns {data-width=400}
-----------------------------------------------------------------------
```{r, include=FALSE}
# evolution temporelle
requin <- read.csv("attacks.csv")
req <- requin %>%
select("Date","Year", "Type", "Country", "Area","Activity", "Fatal..Y.N.") %>%
filter(Year >= 1930)
req3<- req[req$Country %in% c("USA","AUSTRALIA", "SOUTH AFRICA"),]
#nbr des attaques par annee
val <- req3 %>% count(Year,Type, sort = TRUE)
#Types of attacks
val[val=="Boating"]<-NA
val <- val %>%
mutate_at(c('Type'), ~replace_na(.,"Boat"))
#
val[val==""]<-NA
val <- val %>%
mutate_at(c('Type'), ~replace_na(.,"Boat"))
#graph
nbatq <- ggplot(val, aes(Year,n), alpha= Country ) + geom_line(aes( color=Type))+ggtitle("Evolution des attaques des requins pour les 3 premiers Pays") +ylab("Nb Attaques") + xlab("Année")+theme(plot.title = element_text(face="bold",color = "#607c8e", size = 15, hjust=0.5,vjust=1, family = "Serif"),
legend.position = c(0.2, 0.8),
axis.title.x = element_text( face = "bold",size=10),
axis.title.y = element_text(face = "bold",size=10),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank()
) +scale_y_continuous(breaks = seq(0, 88, by = 10)) + scale_colour_manual(values=c("#DAF7A6", "#E5E4E2", "#FFA500","#C70039", "#E97451", "#6495ED"))
```
```{r, include=FALSE}
typ2 <- req3 %>% group_by(Country) %>% count(Year,Type, sort = TRUE)
#
typ2[typ2=="Boating"]<-NA
typ2 <- typ2 %>%
mutate_at(c('Type'), ~replace_na(.,"Boat"))
#
typ2[typ2==""]<-NA
typ2 <- typ2 %>%
mutate_at(c('Type'), ~replace_na(.,"Boat"))
g <- ggplot(typ2, aes(x=Type, y=n, fill=Country)) +geom_boxplot(outlier.shape=NA)+
scale_fill_manual(values=c("darkgoldenrod1","darkred", "#002868")) + ylab("Nb Attaques")+
theme(axis.text.x = element_text(angle = 90, hjust = 1), panel.background = element_rect(fill = "white"),
panel.grid.major = element_line(colour = "#bababa"))+ scale_y_continuous(breaks = seq(0, 88, by = 10)) +ggtitle(" Distribution Types d'attaques par Pays")
```
### Focus sur les 3 pays les plus touchés
```{r}
g
```
### Évolution temporelle
```{r}
ggplotly(nbatq)
```
La nature des attaques {data-icon="fas fa-fish"}
=====================================
```{r,include=FALSE}
req$Country <- tolower(req$Country)
req_pie <- req %>%
filter(Country %in% c("usa", "australia", "south africa"))
req_pie[which(req_pie$Fatal..Y.N. !="Y"),]$Fatal..Y.N. <- "Autres"
req_pie[which(req_pie$Type == "Questionable"),]$Type <- "Invalid"
# netoyage du jeu de donnée
# diving
for (i in (1:nrow(req_pie))) {
if (grepl("diving", req_pie$Activity[i]) | (grepl("Diving", req_pie$Activity[i])) |
(grepl("Dived", req_pie$Activity[i]))) {
req_pie$Activity[i] <- "Diving"
}
}
# fishing
for (i in (1:nrow(req_pie))) {
if (grepl("fishing", req_pie$Activity[i]) | (grepl("Fishing", req_pie$Activity[i])) |
(grepl("lobsters", req_pie$Activity[i])) |
grepl("fish", req_pie$Activity[i]) |
grepl("bichiques", req_pie$Activity[i]) |
grepl("Casting", req_pie$Activity[i]) |
grepl("sardines", req_pie$Activity[i]) |
grepl("Crabbing", req_pie$Activity[i]) |
grepl("Clamming", req_pie$Activity[i]) |
grepl("turtle", req_pie$Activity[i]) |
grepl("prawns", req_pie$Activity[i])|
grepl("net", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "fishing"
}
}
# air or sea disaster
for (i in (1:nrow(req_pie))) {
if (grepl("disaster", req_pie$Activity[i]) | grepl("Disaster", req_pie$Activity[i])|
(grepl("air", req_pie$Activity[i])) | (grepl("plane", req_pie$Activity[i]))
| (grepl("757", req_pie$Activity[i]))|
grepl("hurricane", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "Air/Sea disaster"
}
}
# boat/ship accident de bateau
for (i in (1:nrow(req_pie))) {
if (grepl("Boat", req_pie$Activity[i]) | (grepl("boat", req_pie$Activity[i]))
| (grepl("ship", req_pie$Activity[i])) | (grepl("Fell", req_pie$Activity[i]))
| (grepl("Vessel", req_pie$Activity[i])) |
grepl("sank", req_pie$Activity[i])|
grepl("promotional film ", req_pie$Activity[i]) |
grepl("Ferry", req_pie$Activity[i])|
grepl("ferry", req_pie$Activity[i])|
grepl("capsiz", req_pie$Activity[i]) |
grepl("MV Dona Marilyn", req_pie$Activity[i]) |
grepl("schooner Elizabeth", req_pie$Activity[i]) |
grepl("Towing", req_pie$Activity[i]) |
grepl("Tzenny Chandris", req_pie$Activity[i]) |
grepl("Wreck", req_pie$Activity[i]) |
grepl("wreck", req_pie$Activity[i]) |
grepl("dinghy", req_pie$Activity[i]) |
grepl("yacht", req_pie$Activity[i]) |
grepl("Chumming", req_pie$Activity[i]) |
grepl("raft", req_pie$Activity[i]) |
grepl("Tagging", req_pie$Activity[i]) |
grepl("overboard", req_pie$Activity[i]) |
grepl("free the shark", req_pie$Activity[i]) |
grepl("rescue a shark", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "boat/ship accident"
}
}
# sport
for (i in (1:nrow(req_pie))) {
if (grepl("surf", req_pie$Activity[i]) | grepl("Surf", req_pie$Activity[i]) |(grepl("boarding", req_pie$Activity[i]))|
grepl("Boarding", req_pie$Activity[i]) |
(grepl("batching", req_pie$Activity[i]))| (grepl("Bathing", req_pie$Activity[i])) |
(grepl("bathing", req_pie$Activity[i])) |
(grepl("Swim", req_pie$Activity[i]))| (grepl("swim", req_pie$Activity[i])) |
(grepl("fell", req_pie$Activity[i])) | (grepl("Paddling", req_pie$Activity[i]))|
(grepl("Paddling", req_pie$Activity[i])) | (grepl("water", req_pie$Activity[i]))
| (grepl("Snorkeling", req_pie$Activity[i]))
| (grepl("Sitting", req_pie$Activity[i])) |
grepl("Happy Jack", req_pie$Activity[i]) |
grepl("Bending", req_pie$Activity[i]) |
grepl("Canoe", req_pie$Activity[i]) |
grepl("paddling", req_pie$Activity[i]) |
grepl("Kaya", req_pie$Activity[i]) |
grepl("Lifesaving", req_pie$Activity[i]) |
grepl("Snorkeling", req_pie$Activity[i]) |
grepl("Stamding", req_pie$Activity[i])|
grepl("Wading", req_pie$Activity[i]) |
grepl("Wash", req_pie$Activity[i]) |
grepl("Workong", req_pie$Activity[i])|
grepl("McIver", req_pie$Activity[i]) |
grepl("Standing", req_pie$Activity[i]) |
grepl("Crossing the bay", req_pie$Activity[i])|
grepl("Walking", req_pie$Activity[i]) |
grepl("kill a shark", req_pie$Activity[i]) |
grepl("Removing shark", req_pie$Activity[i]) |
grepl("Riding", req_pie$Activity[i]) |
grepl("Board sailing", req_pie$Activity[i])|
grepl("Exercising", req_pie$Activity[i]) |
grepl("Feeding sharks", req_pie$Activity[i]) |
grepl("Filming", req_pie$Activity[i]) |
grepl("ski", req_pie$Activity[i]) |
grepl("Jumping", req_pie$Activity[i]) |
grepl("Paddle", req_pie$Activity[i]) |
grepl("Play", req_pie$Activity[i]) |
grepl("Resting", req_pie$Activity[i]) |
grepl("floatation device", req_pie$Activity[i])|
grepl("Sculling", req_pie$Activity[i]) |
grepl("Splashing", req_pie$Activity[i]) |
grepl("Dr. Marais", req_pie$Activity[i]) |
grepl("Wrangling", req_pie$Activity[i]) |
grepl("Photographing", req_pie$Activity[i]) |
grepl("SUP", req_pie$Activity[i]) |
grepl("Kakay", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "sport/ activity" }
}
# adrift
for (i in (1:nrow(req_pie))) {
if (grepl("Adrift", req_pie$Activity[i]) | grepl("adrift", req_pie$Activity[i]) |
grepl("Floating", req_pie$Activity[i])|
grepl("3 men & 2 boys", req_pie$Activity[i]) |
grepl("float", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "Adrift"
}
}
# murder / war
for (i in (1:nrow(req_pie))) {
if (grepl("Murder", req_pie$Activity[i]) ||
grepl("submarine", req_pie$Activity[i]) |
grepl("American", req_pie$Activity[i]) |
grepl("torpedoed", req_pie$Activity[i]) |
grepl("Torpedoed", req_pie$Activity[i]) |
grepl("soldiers", req_pie$Activity[i]) |
grepl("Escaping", req_pie$Activity[i]) |
grepl("Japanese", req_pie$Activity[i]) |
grepl("Japan", req_pie$Activity[i]) |
grepl("Sinking", req_pie$Activity[i]) |
grepl("SS", req_pie$Activity[i]) |
grepl("foundered", req_pie$Activity[i]) |
grepl("US", req_pie$Activity[i])
) {
req_pie$Activity[i] <- "murder / war "
}
}
```
```{r, include=FALSE}
# on se concentre sur les 3 pays les plus touchés
#pie1
req_pie1 <- req_pie %>%
group_by(Type) %>%
summarise(Effectif=n(), Pourcentage = Effectif/3480,
ypos = (cumsum(Pourcentage) - 0.5*Pourcentage))
req_pie1 <- req_pie1[-1,]
req_pie1$Type
df1 <- req_pie1 %>%
mutate(csum = rev(cumsum(rev(Pourcentage))),
pos = Pourcentage/2 + lead(csum, 1),
pos = if_else(is.na(pos), Pourcentage/2, pos))
# pie 1 Répartion des Types d'attaques
pie <- ggplot(data = req_pie1, aes(x="", y=Pourcentage, fill= Type)) +
geom_bar(stat="identity", width=1, color= "white") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values = c("Boat" = "#DAF7A6",
"Invalid" = "#E5E4E2",
"Provoked" = "#FFA500",
"Sea Disaster" = "#E97451",
"Unprovoked" = "#6495ED")[]) +
geom_label_repel(data = df1,
aes(y = pos, label = paste0(round(Pourcentage*100,1), "%")),
size = 2, nudge_x = 1, show.legend = FALSE) +
guides(fill = guide_legend(title = "Type")) +
labs(title = "Répartition des Types d'attaques") +
theme(plot.title = element_text(size=9),
legend.text = element_text(size = 5))
# geom_text(aes(label = Type),
# position = position_stack(vjust = 0.5), color="white")
pie
```
```{r, include=FALSE}
req_pie3 <- req_pie %>%
filter(Type =="Unprovoked", Activity!="") %>%
group_by(Fatal..Y.N.) %>%
summarise(Effectif=n(), Pourcentage = Effectif/2444,
ypos = (cumsum(Pourcentage) - 0.5*Pourcentage))
req_pie3$Fatal..Y.N.[which(req_pie3$Fatal..Y.N. =="Y")] <- "Mortel"
req_pie3$Fatal..Y.N.[which(req_pie3$Fatal..Y.N. =="Autres")] <- "Non Mortel"
df3 <- req_pie3 %>%
mutate(csum = rev(cumsum(rev(Pourcentage))),
pos = Pourcentage/2 + lead(csum, 1),
pos = if_else(is.na(pos), Pourcentage/2, pos))
df3$pos[2] <- 0.95
pie3 <- ggplot(data = req_pie3, aes(x="", y=Pourcentage, fill= Fatal..Y.N.)) +
geom_bar(stat="identity", width=1, color= "white") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values = c("Mortel" = "#FC4E00",
"Non Mortel" = "#D4D3DC")) +
geom_label_repel(data = df3,
aes(y = pos, label = paste0(round(Pourcentage*100,1), "%")),
size = 2, nudge_x = 1, show.legend = FALSE) +
guides(fill = guide_legend(title = "Type")) +
labs(title = "Intensité de dégâts des attaques 'unprovoked'") +
theme(plot.title = element_text(size=9),
legend.text = element_text(size = 5))
```
```{r, include=FALSE}
pie
```
```{r, include=F}
req_pie2 <- req_pie %>%
filter(Type =="Unprovoked", Activity!="") %>%
group_by(Activity) %>%
summarise(Effectif=n(), Pourcentage = Effectif/2448)
df2 <- req_pie2 %>%
mutate(csum = rev(cumsum(rev(Pourcentage))),
pos = Pourcentage/2 + lead(csum, 1),
pos = if_else(is.na(pos), Pourcentage/2, pos))
req_pie2$Activity
pie2 <- ggplot(data = req_pie2, aes(x="", y=Pourcentage, fill= Activity)) +
geom_bar(stat="identity", width=1, color= "white") +
coord_polar("y", start=0) +
theme_void() +
scale_fill_manual(values = c("Adrift" = "#B6D8F2",
"Air/Sea disaster" = "#5784BA",
"boat/ship accident" = "#7C4C53",
"Diving" = "#00A0B0",
"fishing" = "#2a9d8f",
"murder / war " = "#BD3100",
"sport/ activity" = "#F5CB5C")) +
geom_label_repel(data = df2,
aes(y = pos, label = paste0(round(Pourcentage*100,1), "%")),
size = 2, nudge_x = 1, show.legend = FALSE) +
guides(fill = guide_legend(title = "Activity")) +
labs(title = "Activités des victimes lors de l'attaque") +
theme(plot.title = element_text(size=9),
legend.text = element_text(size = 5))
```
```{r}
plot_grid(pie, pie3,"",pie2, ncol = 2, nrow = 2)
```
